The Shifting Speeds of Urbanization (1960-2023)

Annual growth rates show when countries urbanized fastest and when they slowed down

30DayChartChallenge
Data Visualization
R Programming
2025
Exploring the dynamic rates of urbanization across eight countries from 1960 to 2023 using World Bank data. This visualization reveals when countries underwent their most rapid urban transitions and when urbanization slowed or reversed.
Published

April 20, 2025

Figure 1: A faceted area chart titled ‘The Shifting Speeds of Urbanization (1960-2023)’ showing annual percentage point changes in urban population for eight countries. Countries are arranged by their maximum rate of urbanization, with Japan showing the highest spike (over 1.5%) around 2000, followed by China with sustained high rates, and Brazil with early high rates that gradually decreased. Nigeria, India, Egypt, the United States, and Germany show more moderate urbanization rates. Negative urbanization periods appear in orange for countries like China, Egypt, and Germany.

Steps to Create this Graphic

1. Load Packages & Setup

Show code
## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
pacman::p_load(
  tidyverse,      # Easily Install and Load the 'Tidyverse'
  ggtext,         # Improved Text Rendering Support for 'ggplot2'
  showtext,       # Using Fonts More Easily in R Graphs
  janitor,        # Simple Tools for Examining and Cleaning Dirty Data
  skimr,          # Compact and Flexible Summaries of Data
  scales,         # Scale Functions for Visualization
  lubridate,      # Make Dealing with Dates a Little Easier
  WDI,            # World Development Indicators and Other World Bank Data
  camcorder       # Record Your Plot History
  )
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  = 8,
    height = 8,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))

2. Read in the Data

Show code
# Search for urbanization indicators
urbanization_indicators <- WDIsearch("urban population")
# head(urbanization_indicators)

# Select countries of interest
countries <- c("USA", "CHN", "IND", "BRA", "NGA", "DEU", "JPN", "EGY")

# Get the data
urban_data <- WDI(
  indicator = "SP.URB.TOTL.IN.ZS",    # Urban population (% of total population)
  country = countries,
  start = 1960,
  end = 2024,  
  extra = TRUE  
)

3. Examine the Data

Show code
glimpse(urban_data)
skim(urban_data)

4. Tidy Data

Show code
### |- Tidy ----
# Clean and prepare the initial dataset
urban_data_clean <- urban_data |>
  select(country, year, SP.URB.TOTL.IN.ZS, region) |>
  rename(urban_population_pct = SP.URB.TOTL.IN.ZS) |>
  filter(!is.na(urban_population_pct))

# Calculate annual change rates
urban_change <- urban_data_clean |>
  arrange(country, year) |>
  group_by(country) |>
  mutate(change_rate = c(NA, diff(urban_population_pct))) |>
  filter(!is.na(change_rate)) |> 
  ungroup()

# Calculate maximum change for each country (for ordering)
country_max_change <- urban_change |>
  group_by(country) |>
  summarize(max_change = max(change_rate, na.rm = TRUE)) |>
  arrange(desc(max_change)) |> 
  ungroup()

# Apply the ordering to each country
urban_change <- urban_change |>
  mutate(country = factor(country, levels = country_max_change$country))

# Create separate dataframes for positive and negative changes
urban_change_pos <- urban_change |>
  mutate(y_value = pmax(0, change_rate))

urban_change_neg <- urban_change |>
  mutate(y_value = pmin(0, change_rate))

5. Visualization Parameters

Show code
### |-  plot aesthetics ----
colors <- get_theme_colors(
  palette = c(
    "pos_color" = "#1B9E77",
    "neg_color" = "#D95F02"  
  )
)

### |-  titles and caption ----
# text
title_text    <- str_wrap("The Shifting Speeds of Urbanization (1960-2023)",
                          width = 55) 

subtitle_text <- str_wrap("Annual growth rates show when countries urbanized fastest and when they slowed down",
                          width = 85)

caption_text <- create_dcc_caption(
  dcc_year = 2025,
  dcc_day = 20,
  source_text =  "{ WDI } World Bank data in R" 
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(

    # Axis elements
    axis.title.y = element_text(color = colors$text, size = rel(0.8), margin = margin(r = 10)),
    axis.text = element_text(color = colors$text, size = rel(0.7)),

    # Grid elements
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank(),
    panel.grid.major.y = element_line(color = "gray92"),

    # Legend elements
    legend.position = "plot",
    legend.title = element_blank(),
    legend.text = element_text(family = fonts$text, size = rel(0.7)),
    
    # Facet elements
    strip.background = element_rect(fill = "gray90", color = NA),
    strip.text = element_text(face = "bold", size = rel(1), margin = margin(10, 0, 10, 0)),
    
    # Plot margins 
    plot.margin = margin(t = 10, r = 20, b = 10, l = 20),
  )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code
### |-  Plot ----
p <- ggplot() +
  # Geoms
  geom_area(
    data = urban_change_pos, 
    aes(x = year, y = y_value), 
    fill = colors$palette[1], alpha = 0.7
  ) +
  geom_area(
    data = urban_change_neg, 
    aes(x = year, y = y_value), 
    fill = colors$palette[2], alpha = 0.7 
  ) +
  geom_line(
    data = urban_change, 
    aes(x = year, y = change_rate), 
    color = "black", 
    linewidth = 0.5
  ) +
  geom_hline(
    yintercept = 0, 
    linetype = "dashed", 
    color = "black", 
    linewidth = 0.5
  ) +
  # Scales
  scale_x_continuous(
    breaks = seq(1960, 2020, by = 20)
  ) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = "Year",
    y = "Change in Urban Population (%)"
  ) +
  # Facet 
  facet_wrap(~ country, ncol = 3)  +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.8),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_text(
      size = rel(0.85),
      family = fonts$subtitle,
      color = colors$subtitle,
      lineheight = 1.2,
      margin = margin(t = 5, b = 15)
    ),
    plot.caption = element_markdown(
      size = rel(0.6),
      family = fonts$caption,
      color = colors$caption,
      lineheight = 0.65,
      hjust = 0.5,
      halign = 0.5,
      margin = margin(t = 10, b = 5)
    ),
  )

7. Save

Show code
### |-  plot image ----  

save_plot(
  p, 
  type = "30daychartchallenge", 
  year = 2025, 
  day = 20, 
  width = 8, 
  height = 8
  )

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      camcorder_0.1.0 WDI_2.7.9       scales_1.3.0   
 [5] skimr_2.1.5     janitor_2.2.0   showtext_0.9-7  showtextdb_3.0 
 [9] sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0  
[13] stringr_1.5.1   dplyr_1.1.4     purrr_1.0.2     readr_2.1.5    
[17] tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] gtable_0.3.6      xfun_0.49         htmlwidgets_1.6.4 tzdb_0.4.0       
 [5] vctrs_0.6.5       tools_4.4.0       generics_0.1.3    curl_6.0.0       
 [9] gifski_1.32.0-1   fansi_1.0.6       pacman_0.5.1      pkgconfig_2.0.3  
[13] lifecycle_1.0.4   farver_2.1.2      compiler_4.4.0    textshaping_0.4.0
[17] munsell_0.5.1     repr_1.1.7        codetools_0.2-20  snakecase_0.11.1 
[21] htmltools_0.5.8.1 yaml_2.3.10       pillar_1.9.0      magick_2.8.5     
[25] commonmark_1.9.2  tidyselect_1.2.1  digest_0.6.37     stringi_1.8.4    
[29] labeling_0.4.3    rsvg_2.6.1        rprojroot_2.0.4   fastmap_1.2.0    
[33] grid_4.4.0        colorspace_2.1-1  cli_3.6.3         magrittr_2.0.3   
[37] base64enc_0.1-3   utf8_1.2.4        withr_3.0.2       timechange_0.3.0 
[41] rmarkdown_2.29    ragg_1.3.3        hms_1.1.3         evaluate_1.0.1   
[45] knitr_1.49        markdown_1.13     rlang_1.1.4       gridtext_0.1.5   
[49] Rcpp_1.0.13-1     glue_1.8.0        xml2_1.3.6        renv_1.0.3       
[53] svglite_2.1.3     rstudioapi_0.17.1 jsonlite_1.8.9    R6_2.5.1         
[57] systemfonts_1.1.0

9. GitHub Repository

The complete code for this analysis is available in 30dcc_2025_20.qmd.

For the full repository, click here.

10. References

  1. Data Sources:
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